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Efficient data redistribution for malleable applications
dc.contributor.author | Martín Álvarez, Iker | |
dc.contributor.author | Aliaga Estellés, José Ignacio | |
dc.contributor.author | Castillo, Maribel | |
dc.contributor.author | Iserte, Sergio | |
dc.date.accessioned | 2024-02-13T20:25:34Z | |
dc.date.available | 2024-02-13T20:25:34Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | MARTÍN ÁLVAREZ, Iker, et al. Efficient data redistribution for malleable applications. In: Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. 2023. p. 416-426 | ca_CA |
dc.identifier.isbn | 979840070785 | |
dc.identifier.uri | http://hdl.handle.net/10234/205853 | |
dc.description | In Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC-W 2023), November 12–17, 2023, Denver, CO, USA. ACM, New York, NY, USA. | ca_CA |
dc.description.abstract | Process malleability can be defined as the ability of a distributed MPI parallel job to change the number of processes on–the–fly without stopping its execution, reallocating the compute resources originally assigned to the job, and without storing application data to disk. MPI malleability consists of four stages: resource reallocation, process management, data redistribution and execution resuming. Among them, data redistribution is the most time-consuming and determines the reconfiguration time. In this paper, we compare different implementations of this stage using point-to-point and collective MPI operations, and discuss the impact of overlapping computation-communication. We then combine these strategies with different methods to expand/shrink jobs, using a synthetic application to emulate MPI-based codes and their malleable counterparts, in order to evaluate the effect of different malleability methods in parallel distributed applications. The results show that the use of asynchronous techniques speeds up execution by 1.14 and 1.21, depending on the network used. | ca_CA |
dc.description.sponsorShip | Funding for open access charge: CRUE-Universitat Jaume I | |
dc.format.extent | 11 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | ACM Digital Library | ca_CA |
dc.publisher | Association for Computing Machinery | ca_CA |
dc.relation.isPartOf | In: Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. 2023. p. 416-426. | ca_CA |
dc.rights | © 2023 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | MPI | ca_CA |
dc.subject | malleability | ca_CA |
dc.subject | data redistribution | ca_CA |
dc.subject | emulations | ca_CA |
dc.title | Efficient data redistribution for malleable applications | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.identifier.doi | https://doi.org/10.1145/3624062.3624110 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://dl.acm.org/doi/10.1145/3624062.3624110 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia, Innovación y Universidades | ca_CA |
project.funder.name | Agencia Estatal de Investigación | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | Comisión Europea | ca_CA |
project.funder.name | Unión Europea | ca_CA |
project.funder.name | Ministerio de Pesca | ca_CA |
project.funder.name | European Union NextGenera-tionEU/PRTR | ca_CA |
oaire.awardNumber | PID2020-113656RB-C21 | ca_CA |
oaire.awardNumber | ACIF/2021/260 | ca_CA |
oaire.awardNumber | 955606 | ca_CA |
oaire.awardNumber | PCI2021-121958 | ca_CA |